The prime effort in research on cancer management is directed toward the development and improvement of innovative therapeutics. Unfortunately, while many experimental anticancer therapies show promising results in preclinical studies, they fail to reproduce the results in the clinical setting. For instance less than 5% of treatments that enter clinical trials go on to be approved for use in humans and over 70% of all drug development costs are attributed to costs associated with failed drugs. One of the key components in this high failure rate is that the vast majority of preclinical studies are performed using animal models based on laboratory mice that are associated with several limitations that account at least in part for the reduced predictive value for human cancer and modest efficacy of current therapies.
A major limitation of laboratory mice-based animal models in cancer-related studies is related to their inbred nature. By not representing wild type (outbred) genomes they hardly reflect either human or even wild type mouse populations. Thus, they can model only a fraction of the natural human population and to that end the discrepancies between the results of preclinical studies in mice and those of studies in patients are not surprising. This issue can be particularly relevant to the pharmacological studies in which the activity of a given drug can be tightly related to the genetic makeup of the host.
Efforts to overcome this limitation have been undertaken, such as through the establishment of the “collaborative cross” in Mus that through a series of designed successive breeding aimed to develop a genetically heterogeneous mouse population with defined characteristics. Although informative to some extent, intrinsic limitations restrict its value due primarily to the fact that along with 3 wild-derived strains, 5 inbred strains participate in the maintenance of this population.
Such issues concerning the low predictive value of animal models for anticancer therapies mandates the development of preclinical models with better predictive value. Development of experimental models that better simulate the human disease and therefore exhibit increased predictive value during preclinical testing would be of considerable value. For instance, use of naturally occurring outbred animal populations that mimic the human cancer process may overcome limitations and may provide experimental models that better represent naturally occurring wild type populations offering genetic variation similar to that seen in human populations and improved preclinical study outcomes.